A method and system for detecting plagiarism of software source code. In one embodiment, a first set of arrays and a second set of arrays are created for a first program source code file and a second program source code file respectively. Each pair of arrays in the first and second sets has entries corresponding to program elements of a distinct program element type such as functional program code, program comments, or program code identifiers. Next, each pair of arrays from the first and second sets is compared to find similar entries, and an intermediate match score is calculated for each pair of arrays based on the similar entries. Further, the resulting intermediate match scores are combined to produce a combined match score, which is then used to provide an indication of copying with respect to the first program source code file and the second program source code file.
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4. A computer-implemented method comprising:
creating, by a computing device executing a detector, a first set of arrays based on a first program source code file including a plurality of program elements;
creating, by the computing device, a second set of arrays for a second program source code file;
comparing the arrays of the first set with the arrays of the second set to find similar entries;
calculating a plurality of intermediate match scores based on the similar entries, the plurality of intermediate match scores including a first intermediate match score calculated based on comparing a first array of the first set of arrays with a second array of the second set of arrays to find entries in the first array that contain similar first words as entries in the second array while ignoring subsequent words in the entries;
combining the plurality of intermediate match scores to produce a combined match score; and
providing an indication of copying with respect to the first program source code file and the second program source code file, wherein the indication of copying is defined by the combined match score.
15. A computer-readable storage medium storing executable instructions to cause a computer system to perform a method comprising:
creating, by a computing device executing a detector, a first set of arrays based on a first program source code file including a plurality of program elements;
creating, by the computing device, a second set of arrays for a second program source code file;
comparing the arrays of the first set with the arrays of the second set to find similar entries;
calculating a plurality of intermediate match scores based on the similar entries, the plurality of intermediate match scores including a first intermediate match score calculated based on comparing a first array of the first set of arrays with a second array of the second set of arrays to find entries in the first array that contain similar first words as entries in the second array while ignoring subsequent words in the entries;
combining the plurality of intermediate match scores to produce a combined match score; and
providing an indication of copying with respect to the first program source code file and the second program source code file, wherein the indication of copying is defined by the combined match score.
1. A computer-implemented method comprising:
creating, by a computing device executing a detector, a first array based on a first program source code file including a plurality of program elements, the first array having entries corresponding to lines of functional program code from the first program source code file;
creating, by the computing device, a second array based on a second program source code file including a plurality of program elements, the second array having entries corresponding to lines of functional program code from the second program source code file;
comparing first words in entries of the first array with first words in entries of the second array while ignoring subsequent words in the entries of the first array and in the entries of the second array;
finding a longest sequence of similar entries between the first array and the second array based on the comparing of the first words in the entries of the first array with the first words in the entries of the second array;
calculating a match score based on a number of lines in the longest sequence; and
providing an indication of copying with respect to the first program source code file and the second program source code file, wherein the indication of copying is defined by the match score.
26. A computer-readable storage medium storing executable instructions to cause a computer system to perform a method comprising:
creating, by the computer system, a first array on a first program source code file including a plurality of program elements, the first array having entries corresponding to lines of functional program code from the first program source code file;
creating, by the computer system, a second array based on a second program source code file including a plurality of program elements, the second array having entries corresponding to lines of functional program code from the second program source code file;
comparing first words in entries of the first array with first words in entries of the second array while ignoring subsequent words in the entries of the first array and in the entries of the second array;
finding a longest sequence of similar entries between the first array and the second array based on the comparing of the first words in the entries of the first array with the first words in the entries of the second array;
calculating a match score based on a number of lines in the longest sequence; and
providing an indication of copying with respect to the first program source code file and the second program source code file, wherein the indication of copying is defined by the match score.
2. The method of
presenting a report identifying the first program source file and the second program source code file, and the match score calculated based on the comparison; and
allowing a user to select the match score to view the similar entries.
3. The method of
5. The method of
6. The method of
presenting a report identifying the first program source file and the second program source code file, and the match score calculated based on the comparison; and
allowing a user to select the match score to view the similar entries.
7. The method of
8. The method of
9. The method of
10. The method of
11. The method of
an array from the first set and an array from the second set have a program element type represented by program code identifiers;
at least some of the similar entries comprise similar program code identifiers; and
calculating an intermediate match score comprises computing a number of the similar program code identifiers.
12. The method of
13. The method of
comparing the first functional program code array with the second functional program code array comprises finding a longest sequence of similar entries, wherein at least some similar entries are different from one another but perform logically equivalent functions; and
calculating the first intermediate match score comprises finding a number of lines contained in the longest sequence.
14. The method of
16. The computer-readable storage medium of
17. The computer-readable storage medium of
calculating a second intermediate match score comprises finding a number of matching lines of functional program code in the first functional program code array and the second functional program code array; and
calculating a third intermediate match score comprises finding a number of matching comment lines in a first program comments array of the first set of arrays and a second program comments array of the second set of arrays.
18. The computer-readable storage medium of
an array from the first set and an array from the second set have a program element type represented by program code identifiers;
at least some of the similar entries comprise similar program code identifiers; and
calculating an intermediate match score comprises computing a number of the similar program code identifiers.
19. The method of
20. The computer-readable storage medium of
comparing the first functional program code array and the second functional program code array comprises finding a longest sequence of similar entries, wherein at least some similar entries are different from one another but perform logically equivalent functions; and
calculating the first intermediate match score comprises finding a number of lines contained in the longest sequence.
21. The computer-readable storage medium of
presenting a report identifying the first program source file and the second program source code file, and the match score calculated based on the comparison; and
allowing a user to select the match score to view the similar entries.
22. The computer-readable storage medium of
23. The computer-readable storage medium of
24. The computer-readable storage medium of
25. The computer-readable storage medium of
27. The computer-readable storage medium of
presenting a report identifying the first program source file and the second program source code file, and the match score calculated based on the comparison; and
allowing a user to select the match score to view the similar entries.
28. The computer-readable storage medium of
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This application is a continuation of U.S. patent application Ser. No. 10/720,636, now U.S. Pat. No. 7,503,035, filed Nov. 25, 2003, which is incorporated herein by reference.
1. Field of the Invention
The present invention relates to software tools for comparing program source code files to determine the amount of similarity between the files and to pinpoint specific sections that are similar. In particular, the present invention relates to finding pairs of source code files that have been copied, in full or in part, from each other or from a common third file.
2. Discussion of the Related Art
Plagiarism detection programs and algorithms have been around for a number of years but have gotten more attention recently due to two main factors. One reason is that the Internet and search engines like Google have made source code very easy to obtain. Another reason is the growing open source movement that allows programmers all over the world to write, distribute, and share code. It follows that plagiarism detection programs have become more sophisticated in recent years. An excellent summary of available tools is given by Paul Clough in his paper, “Plagiarism in natural and programming languages: an overview of current tools and technologies.” Clough discusses tools and algorithms for finding plagiarism in generic text documents as well as in programming language source code files. The present invention only relates to tools and algorithms for finding plagiarism in programming language source code files and so the discussion will be confined to those types of tools. Following are brief descriptions of four of the most popular tools and their algorithms.
The Plague program was developed by Geoff Whale at the University of New South Wales. Plague uses an algorithm that creates what is called a structure-metric, based on matching code structures rather than matching the code itself. The idea is that two pieces of source code that have the same structures are likely to have been copied. The Plague algorithm ignores comments, variable names, function names, and other elements that can easily be globally or locally modified in an attempt to fool a plagiarism detection tool.
Plague has three phases to its detection, as illustrated in
In the first phase 101, a sequence of tokens and structure metrics are created to form a structure profile for each source code file. In other words, each program is boiled down to basic elements that represent control structures and data structures in the program.
In the second phase 102, the structure profiles are compared to find similar code structures. Pairs of files with similar code structures are moved into the next stage.
In the final stage 103, token sequences within matching source code structures are compared using a variant of the Longest Common Subsequence (LCS) algorithm to find similarity.
Clough points out three problems with Plague:
Plague is hard to adapt to new programming languages because it is so dependent on expert knowledge of the programming language of the source code it is examining. The tokens depend on specific language statements and the structure metrics depend on specific programming language structures.
The output of Plague consists of two indices H an HT that needs interpretation. While the output of each plagiarism detection program presented here relies on expert interpretation, results from Plague are particularly obscure.
Plague uses UNIX shell tools for processing, which makes it slow. This is not an innate problem with the algorithm, which can be ported to compiled code for faster processing.
There are other problems with Plague:
Plague is vulnerable to changing the order of code lines in the source code.
Plague throws out useful information when it discards comments, variable names, function names, and other identifiers.
The first point is a problem because code sections can be rearranged and individual lines can be reordered to fool Plague into giving lower scores or missing copied code altogether. This is one method that sophisticated plagiarists use to hide malicious code theft.
The second point is a problem because comments, variable names, function names, and other identifiers can be very useful in finding plagiarism. These identifiers can pinpoint copied code immediately. Even in many cases of intentional copying, comments are left in the copied code and can be used to find matches. Common misspellings or the use of particular words throughout the program in two sets of source code can help identify them as having the same author even if the code structures themselves do not match. As we will see, this is a common problem with these plagiarism tools.
The YAP programs (YAP, YAP2, YAP3) were developed by Michael Wise at the University of Sydney, Australia. YAP stands for “Yet Another Plague” and is an extension of Plague. All three version of YAP use algorithms, illustrated in
In the first phase 201, generate a list of tokens for each source code file.
In the second phase 202, compare pairs of token files.
The first phase of the algorithm is identical for all three programs. The steps of this phase, illustrated in
In step 203 remove comments and string constants.
In step 204 translate upper-case letters to lower-case.
In step 205, map synonyms to a common form. In other words, substitute a basic set of programming language statements for common, nearly equivalent statements. As an example using the C language, the language keyword “strncmp” would be mapped to “strcmp”, and the language keyword “function” would be mapped to “procedure”.
In step 206, reorder the functions into their calling order. The first call to each function is expanded inline and tokens are substituted appropriately. Each subsequent call to the same function is simply replaced by the token FUN.
In step 207, remove all tokens that are not specifically programming language keywords.
The second phase 202 of the algorithm is identical for YAP and YAP2. YAP relied on the sdiff function in UNIX to compare lists of tokens for the longest common sequence of tokens. YAP2, implemented in Perl, improved performance in the second phase 202 by utilizing a more sophisticated algorithm known as Heckel's algorithm. One limitation of YAP and YAP2 that was recognized by Wise was difficulty dealing with transposed code. In other words, functions or individual statements could be rearranged to hide plagiarism. So for YAP3, the second phase uses the Running-Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithm that is more immune to tokens being transposed.
YAP3 is an improvement over Plague in that it does not attempt a full parse of the programming language as Plague does. This simplifies the task of modifying the tool to work with other programming languages. Also, the new algorithm is better able to find matches in transposed lines of code.
There are still problems with YAP3 that need to be noted:
In order to decrease the run time of the program the RKR-GST algorithm uses hashing and only considers matches of strings of a minimal length. This opens up the algorithm to missing some matches.
The tokens used by YAP3 are still dependent on knowledge of the particular programming language of the files being compared.
Although less so than Plague, YAP3 is still vulnerable to changing the order of code lines in the source code.
YAP3 throws out much useful information when it discards comments, variable names, function names, and other identifiers that can and have been used to find source code with common origins.
JPlag is a program, written in Java by Lutz Prechelt and Guido Malpohl of the University Karlsruhe and Michael Philippsen of the University of Erlangen-Nuremberg, to detect plagiarism in Java, Scheme, C, or C++ source code. Like other plagiarism detection programs, JPlag works in phases as illustrated in
There are two steps in the first phase 301. In the first step 303, whitespace, comments, and identifier names are removed. As with Plague and the YAP programs, in the second step 304, the remaining language statements are replaced by tokens.
As with YAP3, the method of Greedy String Tiling is used to compare tokens in different files in the second phase 302. More matching tokens corresponds to a higher degree of similarity and a greater chance of plagiarism.
As can be seen from the description, JPlag is nearly identical in its algorithm to YAP3 though it uses different optimization procedures for reducing runtime. One difference is that JPlag produces a very nice HTML output with detailed plots comparing file similarities. It also allows the user to click on a file combination to bring up windows showing both files with areas of similarity highlighted. The limitations of JPlag are the same limitations that apply to YAP3 that have been listed previously.
The Measure of Software Similarity (MOSS) program was developed at the University of California at Berkeley by Alex Aiken. MOSS uses a winnowing algorithm. The MOSS algorithm can be described by these steps, as illustrated in
In the first step 401, remove all whitespace and punctuation from each source code file and convert all characters to lower case.
In the second step 402, divide the remaining non-whitespace characters of each file into k-grams, which are contiguous substrings of length k, by sliding a window of size k through the file. In this way the second character of the first k-gram is the first character of the second k-gram and so on.
In the third step 403, hash each k-gram and select a subset of all k-grams to be the fingerprints of the document. The fingerprint includes information about the position of each selected k-gram in the document.
In the fourth step 404, compare file fingerprints to find similar files.
An example of the algorithm for creating these fingerprints is shown in
Of all the programs discussed here, MOSS throws out the most information. The algorithm attempts to keep enough critical information to flag similarities. The algorithm is also noted to have a very low occurrence of false positives. The problem using this algorithm for detecting source code plagiarism is that it produces a high occurrence of false negatives. In other words, matches can be missed. The reason for this is as follows:
By treating source code files like generic text files, much structural information is lost that can be used to find matches. For example, whitespace, punctuation, and upper-case characters have significant meaning in programming languages but are thrown out by MOSS.
Smaller k-grams increase the execution time of the program, but increase the sensitivity. MOSS makes the tradeoff of time for efficiency and typically uses a 5-gram. However, many programming language statements are less than 5 characters and can be missed.
Most of the k-grams are also thrown out, reducing the accuracy even further.
Plagiarism of software source code is a serious problem in two distinct areas of endeavor these days—cheating by students at schools and intellectual property theft at corporations. A number of methods have been implemented to check source code files for plagiarism, each with their strengths and weaknesses. One embodiment of the invention provides a method consisting of a combination of algorithms in a single tool to assist a human expert in finding plagiarized code. In some embodiments, two or more of the following algorithms are used to find plagiarism: Source Line Matching, Comment Line Matching, Word Matching, Partial Word Matching, and Semantic Sequence Matching.
Further features and advantages of various embodiments of the present invention are described in the detailed description below, which is given by way of example only.
The present invention will be understood more fully from the detailed description given below and from the accompanying drawings of the preferred embodiment of the invention, which, however, should not be taken to limit the invention to the specific embodiment but are for explanation and understanding only.
The present invention will be understood more fully from the detailed description given below and from the accompanying drawings of the preferred embodiment of the invention, which, however, should not be taken to limit the invention to the specific embodiment but are for explanation and understanding only.
The present invention takes a different approach to plagiarism detection than the programs described previously. The present invention compares features of each pair of source code files completely, rather than using a sampling method for comparing a small number of hashed samples of code. This may require a computer program that implements the present invention to run for hours or in some cases days to find plagiarism among large sets of large files. Given the stakes in many intellectual property theft cases, this more accurate method is worth the processing time involved. And it is certainly less expensive than hiring experts on an hourly basis to manually pore over code by hand.
The present invention makes use of a basic knowledge of programming languages and program structures to simplify the matching task. There is a small amount of information needed in the form of a list of common programming language statements that the present invention must recognize. This list is specific to the programming language being examined. In addition, the present invention needs information on characters that are used to identify comments and characters that are used as separators.
The present invention uses five algorithms to find plagiarism: Source Line Matching, Comment Line Matching, Word Matching, Partial Word Matching, and Semantic Sequence Matching. Each algorithm is useful in finding different clues to plagiarism that the other algorithms may miss. By using all five algorithms, chances of missing plagiarized code is significantly diminished. Before any of the algorithm processing takes place, some preprocessing is done to create string arrays. Each file is represented by three arrays—an array of source lines that consists of lines of functional source code and does not include comments, an array of comment lines that do not include functional source code, and an array of identifiers found in the course code. Identifiers include variable names, constant names, function names, and any other words that are not keywords of the programming language.
In one embodiment of the present invention, each line of each file is initially examined and two string arrays for each file are created: SourceLines1[ ], CommentLines1[ ] and SourceLines2[ ], CommentLines2[ ] are the source lines and comment lines for file 1 and file 2 respectively. Examples of these arrays are shown for a sample code snippet in
Note that blank lines are preserved as null strings in the array. This is done so that the index in each array corresponds to the line number in the original file and matching lines can easily be mapped back to their original files.
Next the source lines are examined from each file to obtain a list of all words in the source code that are not programming language keywords, as shown in part (c) 603 of
Word Matching
For each file pair, this embodiment of the present invention uses a “word matching” algorithm to count the number of matching identifiers—identifiers being words that are not programming language keywords. In order to determine whether a word is a programming language keyword, comparison is done with a list of known programming language keywords. For example, the word “while” in a C source code file would be ignored as a keyword by this algorithm. In some programming languages like C and Java, keywords are case sensitive. In other programming languages like Basic, keywords are not case sensitive. This embodiment has a switch to turn case sensitivity on or off depending on the programming language being examined. So for a case sensitive language like C, the word “While” would not be considered a language keyword and would not be ignored. In a case insensitive language like Basic, the word “While” would be considered a language keyword and would be ignored. In either case, when comparing non-keyword words in the file pairs, case is ignored so that the word “Index” in one file would be matched with the word “index” in the other. This case-insensitive comparison is done to prevent being fooled by simple case changes in plagiarized code in an attempt to avoid detection.
This simple comparison yields a number w representing the number of matching identifier words in the source code of the pair of files. This number is determined by the equation
w=Σ(Ai+fNNi) for i=1 to mw
where mw is the number of case-insensitive matching non-keyword words in the two files, Ai is the number of matching alphabetical characters in matching word i, Ni is the number of matching numerals in matching word i, and fN is a fractional value given to matching numerals in a matching word. The reason for this fractional value is that alphabetical characters are less likely to match by chance, but numerals may match simply because they represent common mathematical constants—the value of pi for example—rather than because of plagiarism. Longer sequences of letters and/or numerals have a smaller probability of matching by chance and therefore deserve more consideration as potential plagiarism.
This algorithm tends to uncover code where common identifier names are used for variables, constants, and functions, implying that the code was plagiarized. Since this algorithm only eliminates standard programming language statements, common library routines that are used on both files will produce a high value of w. Code that uses a large number of the same library routines also has a higher chance of being plagiarized code.
Partial Word Matching
The “partial word matching” algorithm examines each identifier (non-keyword) word in the source code of one file of a file pair and finds all words that match a sequence within one or more non-keyword words in the other file of a file pair. Like the word matching algorithm, this one is also case insensitive. This algorithm is illustrated in
This algorithm works just like the word match algorithm on the list of partially matching words. It yields a number p representing the number of partially matching identifier words in the source code of the pair of files. This number is determined by the equation
p=Σ(Ai+fNNi) for i=1 to mp
where mp is the number of case-insensitive matching partial words in the two files, Ai is the number of matching alphabetical characters in matching partial word i, Ni is the number of matching numerals in matching partial word i, and fN is a fractional value given to matching numbers in a matching partial word.
Source Line Matching
The “source line matching” algorithm compares each line of source code from both files, ignoring case. We refer to functional program language lines as source lines and exclude comment lines. Also, sequences of whitespace are converted to single spaces so that the syntax structure of the line is preserved. Note that a line of source code may have a comment at the end, in which case the comment is stripped off for this comparison. Source lines that contain only programming language keywords are not examined. For source lines to be considered matches, they must contain at least one non-keyword such as a variable name or function name. Otherwise, lines containing basic operations would be reported as matching.
This algorithm yields a number s representing the number of matching source lines in the pair of files.
Comment Line Matching
The “comment line matching” algorithm compares each line of comments from both files, again ignoring case. Note that a line of source code may have a comment at the end. The source code is stripped off for this comparison, leaving only the comment. The entire comment is compared, regardless of whether there are keywords in the comment or not.
This algorithm yields a number c representing the number of matching comment lines in the pair of files.
Semantic Sequence Matching
The “semantic sequence” algorithm compares the first word of every source line in the pair of files, ignoring blank lines and comment lines. This algorithm finds sequences of code that appear to perform the same functions despite changed comments and identifier names. The algorithm finds the longest common semantic sequence within both files. Look at the example code in
Match Score
The entire sequence, applying all five algorithms, is shown in
The single match score t is a measure of the similarity of the file pairs. If a file pair has a higher score, it implies that these files are more similar and may be plagiarized from each other or from a common third file. This score, known as a “total match score,” is given by the following equation.
t=kww+kpp+kss+kcc+kqq
In this equation, each of the results of the five individual algorithms is weighted and added to give a total matching score. These weights must be adjusted to give the optimal results. There is also a sixth weight that is hidden in the above equation and must also be evaluated. That weight is fN, the fractional value given to matching numerals in a matching word or partial word. Thus the weights that must be adjusted to get a useful total matching score are:
fN the fractional value given to matching numerals in a matching word or partial word
kw the weight given to the word matching algorithm
kp the weight given to the partial word matching algorithm
ks the weight given to the source line matching algorithm
kc the weight given to the comment line matching algorithm
kq the weight given to the semantic sequence matching algorithm
These numbers are adjusted by experimentation over time to give the best results. However, unlike the other programs described in this paper, this invention is not intended to give a specific cutoff threshold for file similarity. There are many kinds of plagiarism and many ways of fooling plagiarism detection programs. For this reason, this embodiment of the present invention produces a basic HTML output report with a list of file pairs ordered by their total match scores as shown in
The user can click on a match score hyperlink to bring up a detailed HTML report showing exact matches between the selected file pairs. In this way, experts are directed to suspicious similarities and allowed to make their own judgments. A sample detailed report is shown in
The present invention is not a tool for precisely pinpointing plagiarized code, but rather a tool to assist an expert in finding plagiarized code. The present invention reduces the effort needed by the expert by allowing him to narrow his focus from hundreds of thousands of lines in hundreds of files to dozens of lines in dozens of files.
Various modifications and adaptations of the operations that are described here would be apparent to those skilled in the art based on the above disclosure. Many variations and modifications within the scope of the invention are therefore possible. The present invention is set forth by the following claims.
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